Wafer-map clustering & excursion analysis
Group edge rings, scratch signatures, center clusters, and repeaters into reviewable spatial patterns.
Example outputs shown for illustration. Numbers depend on your samples and protocol.
Image: Illustrative rendering (AI-generated, gpt-image-2), not a production wafer map
Results
Each measurement below comes from your own images, tied back to the annotated frame it came from, so results stay comparable across samples, locations, and conditions.
class
Spatial pattern class
Each detected feature assigned to a class using the agreed rule set, shown on the image.
Example, not a claimed result
In your report: Per-feature class, counts by class, and annotated overlays.
per area
Cluster boundaries
Findings placed in sample coordinates to reveal spatial patterns and local density.
Example, not a claimed result
In your report: Coordinate-referenced map, regional density, and representative images.
per area
Density by region
Findings placed in sample coordinates to reveal spatial patterns and local density.
Example, not a claimed result
In your report: Coordinate-referenced map, regional density, and representative images.
per area
Lot comparison table
Findings placed in sample coordinates to reveal spatial patterns and local density.
In your report: Coordinate-referenced map, regional density, and representative images.
Built around your image set
Use the calibrated images your team already collects, together with the locations you need to compare.
Each report includes
The artifacts your team receives, ready for review and archive.
Annotated image set
Original images with regions of interest, measurement points, masks, and finding overlays.
Measurement table
Location-indexed values, units, distributions, and QC flags in a structured export.
Method record
Calibration evidence, analysis settings, version history, and validation summary.
Confidence in every resultTraceable measurements, reviewed against your agreed reference method.
- Traceable scale
- The report records the image scale, calibration evidence, and a method-specific expanded measurement uncertainty. It does not use one product-wide accuracy number.
- Reference comparison
- The configured method is compared with your accepted reference method or reviewed annotations, and reports bias by measurement range and image condition.
- Detection performance
- Detections are evaluated against reviewed reference regions with precision, recall, and segmentation overlap, separated by the conditions that affect performance.
- Repeatability
- The locked protocol is rerun on the same inputs and on a defined repeat set. The review records variation from image acquisition, sampling, and analysis separately where possible.
What this result does not establish: Groups visible spatial patterns for review. Pattern similarity is not proof of process root cause; confirm it against equipment history, process data, and electrical test.
The measurement, today
Defect maps are often interpreted from experience and compared visually. Similar clusters can be labeled differently between shifts, and the associated review images may be scattered across tools.
What it costs
Spatial signatures can focus a process investigation on a chamber, handling path, or consumable. Consistent pattern labels make cross-lot review faster without claiming root cause from the image alone.
From image to reviewed result
- 1
Load the map and images
Bring in wafer-coordinate findings and the review images that support each candidate.
- 2
Cluster the locations
Group defects by spatial proximity, radial position, orientation, and configured signature rules.
- 3
Review the pattern
Present representative images alongside the ring, scratch, center, or repeating pattern candidate.
- 4
Compare lots
Export pattern labels and regional density changes for engineering review.
Not sure this is the right measurement?
Send a representative image and your measurement goal. A ConductVision scientist will confirm whether this is the right fit, or point you to the closer workflow, before you commit to a quote.
Related applications
Wafer defect map & binning
Detect, locate, size, and bin defects across a wafer into a spatial map.
Semiconductor defect patterns
Classify wafer-map patterns and quantify defect clusters and densities.
CMP scratch & planarization inspection
Locate scratches, residual particles, dishing, and erosion signatures after chemical mechanical planarization.
Send a sample image and a measurement goal
We will show the closest ConductVision workflow and flag what needs custom validation for your images.
